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Merge remote-tracking branch 'upstream/master' into addBatchIndex

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This commit is contained in:
flyingshutter 2023-06-02 15:30:58 +02:00
commit 3ce4dc988d
31 changed files with 2396 additions and 254 deletions
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2
.ci/update_windows/update.py
View File

@ -41,7 +41,7 @@ def pull(repo, remote_name='origin', branch='master'):
else:
raise AssertionError('Unknown merge analysis result')
pygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0)
repo = pygit2.Repository(str(sys.argv[1]))
ident = pygit2.Signature('comfyui', 'comfy@ui')
try:

1
.github/workflows/windows_release_cu118_package.yml vendored
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@ -30,6 +30,7 @@ jobs:
- uses: actions/checkout@v3
with:
fetch-depth: 0
persist-credentials: false
- shell: bash
run: |
cd ..

1
.github/workflows/windows_release_nightly_pytorch.yml vendored
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@ -17,6 +17,7 @@ jobs:
- uses: actions/checkout@v3
with:
fetch-depth: 0
persist-credentials: false
- uses: actions/setup-python@v4
with:
python-version: '3.11.3'

107
comfy/diffusers_convert.py
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@ -1,14 +1,5 @@
import json
import os
import yaml
import folder_paths
from comfy.ldm.util import instantiate_from_config
from comfy.sd import ModelPatcher, load_model_weights, CLIP, VAE
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
@ -262,101 +253,3 @@ def convert_text_enc_state_dict(text_enc_dict):
return text_enc_dict
def load_diffusers(model_path, fp16=True, output_vae=True, output_clip=True, embedding_directory=None):
diffusers_unet_conf = json.load(open(osp.join(model_path, "unet/config.json")))
diffusers_scheduler_conf = json.load(open(osp.join(model_path, "scheduler/scheduler_config.json")))
# magic
v2 = diffusers_unet_conf["sample_size"] == 96
if 'prediction_type' in diffusers_scheduler_conf:
v_pred = diffusers_scheduler_conf['prediction_type'] == 'v_prediction'
if v2:
if v_pred:
config_path = folder_paths.get_full_path("configs", 'v2-inference-v.yaml')
else:
config_path = folder_paths.get_full_path("configs", 'v2-inference.yaml')
else:
config_path = folder_paths.get_full_path("configs", 'v1-inference.yaml')
with open(config_path, 'r') as stream:
config = yaml.safe_load(stream)
model_config_params = config['model']['params']
clip_config = model_config_params['cond_stage_config']
scale_factor = model_config_params['scale_factor']
vae_config = model_config_params['first_stage_config']
vae_config['scale_factor'] = scale_factor
model_config_params["unet_config"]["params"]["use_fp16"] = fp16
unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.safetensors")
vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.safetensors")
text_enc_path = osp.join(model_path, "text_encoder", "model.safetensors")
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
unet_state_dict = load_file(unet_path, device="cpu")
else:
unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
unet_state_dict = torch.load(unet_path, map_location="cpu")
if osp.exists(vae_path):
vae_state_dict = load_file(vae_path, device="cpu")
else:
vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
vae_state_dict = torch.load(vae_path, map_location="cpu")
if osp.exists(text_enc_path):
text_enc_dict = load_file(text_enc_path, device="cpu")
else:
text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
text_enc_dict = torch.load(text_enc_path, map_location="cpu")
# Convert the UNet model
unet_state_dict = convert_unet_state_dict(unet_state_dict)
unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
vae_state_dict = convert_vae_state_dict(vae_state_dict)
vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict
if is_v20_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()}
text_enc_dict = convert_text_enc_state_dict_v20(text_enc_dict)
text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
else:
text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
sd = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
clip = None
vae = None
class WeightsLoader(torch.nn.Module):
pass
w = WeightsLoader()
load_state_dict_to = []
if output_vae:
vae = VAE(scale_factor=scale_factor, config=vae_config)
w.first_stage_model = vae.first_stage_model
load_state_dict_to = [w]
if output_clip:
clip = CLIP(config=clip_config, embedding_directory=embedding_directory)
w.cond_stage_model = clip.cond_stage_model
load_state_dict_to = [w]
model = instantiate_from_config(config["model"])
model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to)
if fp16:
model = model.half()
return ModelPatcher(model), clip, vae

111
comfy/diffusers_load.py Normal file
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@ -0,0 +1,111 @@
import json
import os
import yaml
import folder_paths
from comfy.ldm.util import instantiate_from_config
from comfy.sd import ModelPatcher, load_model_weights, CLIP, VAE
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
import diffusers_convert
def load_diffusers(model_path, fp16=True, output_vae=True, output_clip=True, embedding_directory=None):
diffusers_unet_conf = json.load(open(osp.join(model_path, "unet/config.json")))
diffusers_scheduler_conf = json.load(open(osp.join(model_path, "scheduler/scheduler_config.json")))
# magic
v2 = diffusers_unet_conf["sample_size"] == 96
if 'prediction_type' in diffusers_scheduler_conf:
v_pred = diffusers_scheduler_conf['prediction_type'] == 'v_prediction'
if v2:
if v_pred:
config_path = folder_paths.get_full_path("configs", 'v2-inference-v.yaml')
else:
config_path = folder_paths.get_full_path("configs", 'v2-inference.yaml')
else:
config_path = folder_paths.get_full_path("configs", 'v1-inference.yaml')
with open(config_path, 'r') as stream:
config = yaml.safe_load(stream)
model_config_params = config['model']['params']
clip_config = model_config_params['cond_stage_config']
scale_factor = model_config_params['scale_factor']
vae_config = model_config_params['first_stage_config']
vae_config['scale_factor'] = scale_factor
model_config_params["unet_config"]["params"]["use_fp16"] = fp16
unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.safetensors")
vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.safetensors")
text_enc_path = osp.join(model_path, "text_encoder", "model.safetensors")
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
unet_state_dict = load_file(unet_path, device="cpu")
else:
unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
unet_state_dict = torch.load(unet_path, map_location="cpu")
if osp.exists(vae_path):
vae_state_dict = load_file(vae_path, device="cpu")
else:
vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
vae_state_dict = torch.load(vae_path, map_location="cpu")
if osp.exists(text_enc_path):
text_enc_dict = load_file(text_enc_path, device="cpu")
else:
text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
text_enc_dict = torch.load(text_enc_path, map_location="cpu")
# Convert the UNet model
unet_state_dict = diffusers_convert.convert_unet_state_dict(unet_state_dict)
unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
vae_state_dict = diffusers_convert.convert_vae_state_dict(vae_state_dict)
vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict
if is_v20_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()}
text_enc_dict = diffusers_convert.convert_text_enc_state_dict_v20(text_enc_dict)
text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
else:
text_enc_dict = diffusers_convert.convert_text_enc_state_dict(text_enc_dict)
text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
sd = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
clip = None
vae = None
class WeightsLoader(torch.nn.Module):
pass
w = WeightsLoader()
load_state_dict_to = []
if output_vae:
vae = VAE(scale_factor=scale_factor, config=vae_config)
w.first_stage_model = vae.first_stage_model
load_state_dict_to = [w]
if output_clip:
clip = CLIP(config=clip_config, embedding_directory=embedding_directory)
w.cond_stage_model = clip.cond_stage_model
load_state_dict_to = [w]
model = instantiate_from_config(config["model"])
model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to)
if fp16:
model = model.half()
return ModelPatcher(model), clip, vae

51
comfy/model_management.py
View File

@ -15,9 +15,8 @@ vram_state = VRAMState.NORMAL_VRAM
set_vram_to = VRAMState.NORMAL_VRAM
total_vram = 0
total_vram_available_mb = -1
accelerate_enabled = False
lowvram_available = True
xpu_available = False
directml_enabled = False
@ -31,11 +30,12 @@ if args.directml is not None:
directml_device = torch_directml.device(device_index)
print("Using directml with device:", torch_directml.device_name(device_index))
# torch_directml.disable_tiled_resources(True)
lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default.
try:
import torch
if directml_enabled:
total_vram = 4097 #TODO
pass #TODO
else:
try:
import intel_extension_for_pytorch as ipex
@ -46,7 +46,7 @@ try:
total_vram = torch.cuda.mem_get_info(torch.cuda.current_device())[1] / (1024 * 1024)
total_ram = psutil.virtual_memory().total / (1024 * 1024)
if not args.normalvram and not args.cpu:
if total_vram <= 4096:
if lowvram_available and total_vram <= 4096:
print("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram")
set_vram_to = VRAMState.LOW_VRAM
elif total_vram > total_ram * 1.1 and total_vram > 14336:
@ -92,6 +92,7 @@ if ENABLE_PYTORCH_ATTENTION:
if args.lowvram:
set_vram_to = VRAMState.LOW_VRAM
lowvram_available = True
elif args.novram:
set_vram_to = VRAMState.NO_VRAM
elif args.highvram:
@ -103,18 +104,18 @@ if args.force_fp32:
FORCE_FP32 = True
if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM):
if lowvram_available:
try:
import accelerate
accelerate_enabled = True
vram_state = set_vram_to
if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM):
vram_state = set_vram_to
except Exception as e:
import traceback
print(traceback.format_exc())
print("ERROR: COULD NOT ENABLE LOW VRAM MODE.")
print("ERROR: LOW VRAM MODE NEEDS accelerate.")
lowvram_available = False
total_vram_available_mb = (total_vram - 1024) // 2
total_vram_available_mb = int(max(256, total_vram_available_mb))
try:
if torch.backends.mps.is_available():
@ -199,22 +200,33 @@ def load_model_gpu(model):
model.unpatch_model()
raise e
model.model_patches_to(get_torch_device())
torch_dev = get_torch_device()
model.model_patches_to(torch_dev)
vram_set_state = vram_state
if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
model_size = model.model_size()
current_free_mem = get_free_memory(torch_dev)
lowvram_model_memory = int(max(256 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 ))
if model_size > (current_free_mem - (512 * 1024 * 1024)): #only switch to lowvram if really necessary
vram_set_state = VRAMState.LOW_VRAM
current_loaded_model = model
if vram_state == VRAMState.CPU:
if vram_set_state == VRAMState.CPU:
pass
elif vram_state == VRAMState.MPS:
elif vram_set_state == VRAMState.MPS:
mps_device = torch.device("mps")
real_model.to(mps_device)
pass
elif vram_state == VRAMState.NORMAL_VRAM or vram_state == VRAMState.HIGH_VRAM:
elif vram_set_state == VRAMState.NORMAL_VRAM or vram_set_state == VRAMState.HIGH_VRAM:
model_accelerated = False
real_model.to(get_torch_device())
else:
if vram_state == VRAMState.NO_VRAM:
if vram_set_state == VRAMState.NO_VRAM:
device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "256MiB", "cpu": "16GiB"})
elif vram_state == VRAMState.LOW_VRAM:
device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "{}MiB".format(total_vram_available_mb), "cpu": "16GiB"})
elif vram_set_state == VRAMState.LOW_VRAM:
device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "{}MiB".format(lowvram_model_memory // (1024 * 1024)), "cpu": "16GiB"})
accelerate.dispatch_model(real_model, device_map=device_map, main_device=get_torch_device())
model_accelerated = True
@ -377,7 +389,10 @@ def should_use_fp16():
def soft_empty_cache():
global xpu_available
if xpu_available:
global vram_state
if vram_state == VRAMState.MPS:
torch.mps.empty_cache()
elif xpu_available:
torch.xpu.empty_cache()
elif torch.cuda.is_available():
if torch.version.cuda: #This seems to make things worse on ROCm so I only do it for cuda

35
comfy/sd.py
View File

@ -14,6 +14,7 @@ from .t2i_adapter import adapter
from . import utils
from . import clip_vision
from . import gligen
from . import diffusers_convert
def load_model_weights(model, sd, verbose=False, load_state_dict_to=[]):
m, u = model.load_state_dict(sd, strict=False)
@ -285,15 +286,29 @@ def model_lora_keys(model, key_map={}):
return key_map
class ModelPatcher:
def __init__(self, model):
def __init__(self, model, size=0):
self.size = size
self.model = model
self.patches = []
self.backup = {}
self.model_options = {"transformer_options":{}}
self.model_size()
def model_size(self):
if self.size > 0:
return self.size
model_sd = self.model.state_dict()
size = 0
for k in model_sd:
t = model_sd[k]
size += t.nelement() * t.element_size()
self.size = size
return size
def clone(self):
n = ModelPatcher(self.model)
n = ModelPatcher(self.model, self.size)
n.patches = self.patches[:]
n.model_options = copy.deepcopy(self.model_options)
return n
@ -504,10 +519,16 @@ class VAE:
if config is None:
#default SD1.x/SD2.x VAE parameters
ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
self.first_stage_model = AutoencoderKL(ddconfig, {'target': 'torch.nn.Identity'}, 4, monitor="val/rec_loss", ckpt_path=ckpt_path)
self.first_stage_model = AutoencoderKL(ddconfig, {'target': 'torch.nn.Identity'}, 4, monitor="val/rec_loss")
else:
self.first_stage_model = AutoencoderKL(**(config['params']), ckpt_path=ckpt_path)
self.first_stage_model = AutoencoderKL(**(config['params']))
self.first_stage_model = self.first_stage_model.eval()
if ckpt_path is not None:
sd = utils.load_torch_file(ckpt_path)
if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
sd = diffusers_convert.convert_vae_state_dict(sd)
self.first_stage_model.load_state_dict(sd, strict=False)
self.scale_factor = scale_factor
if device is None:
device = model_management.get_torch_device()
@ -722,7 +743,7 @@ def load_controlnet(ckpt_path, model=None):
use_spatial_transformer=True,
transformer_depth=1,
context_dim=context_dim,
use_checkpoint=True,
use_checkpoint=False,
legacy=False,
use_fp16=use_fp16)
else:
@ -739,7 +760,7 @@ def load_controlnet(ckpt_path, model=None):
use_linear_in_transformer=True,
transformer_depth=1,
context_dim=context_dim,
use_checkpoint=True,
use_checkpoint=False,
legacy=False,
use_fp16=use_fp16)
if pth:
@ -1024,7 +1045,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
}
unet_config = {
"use_checkpoint": True,
"use_checkpoint": False,
"image_size": 32,
"out_channels": 4,
"attention_resolutions": [

93
comfy/utils.py
View File

@ -1,11 +1,16 @@
import torch
import math
import struct
def load_torch_file(ckpt, safe_load=False):
if ckpt.lower().endswith(".safetensors"):
import safetensors.torch
sd = safetensors.torch.load_file(ckpt, device="cpu")
else:
if safe_load:
if not 'weights_only' in torch.load.__code__.co_varnames:
print("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.")
safe_load = False
if safe_load:
pl_sd = torch.load(ckpt, map_location="cpu", weights_only=True)
else:
@ -46,6 +51,88 @@ def transformers_convert(sd, prefix_from, prefix_to, number):
sd[k_to] = weights[shape_from*x:shape_from*(x + 1)]
return sd
def safetensors_header(safetensors_path, max_size=100*1024*1024):
with open(safetensors_path, "rb") as f:
header = f.read(8)
length_of_header = struct.unpack('<Q', header)[0]
if length_of_header > max_size:
return None
return f.read(length_of_header)
def bislerp(samples, width, height):
def slerp(b1, b2, r):
'''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC'''
c = b1.shape[-1]
#norms
b1_norms = torch.norm(b1, dim=-1, keepdim=True)
b2_norms = torch.norm(b2, dim=-1, keepdim=True)
#normalize
b1_normalized = b1 / b1_norms
b2_normalized = b2 / b2_norms
#zero when norms are zero
b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0
b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0
#slerp
dot = (b1_normalized*b2_normalized).sum(1)
omega = torch.acos(dot)
so = torch.sin(omega)
#technically not mathematically correct, but more pleasing?
res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized
res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c)
#edge cases for same or polar opposites
res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5]
res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1]
return res
def generate_bilinear_data(length_old, length_new):
coords_1 = torch.arange(length_old).reshape((1,1,1,-1)).to(torch.float32)
coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear")
ratios = coords_1 - coords_1.floor()
coords_1 = coords_1.to(torch.int64)
coords_2 = torch.arange(length_old).reshape((1,1,1,-1)).to(torch.float32) + 1
coords_2[:,:,:,-1] -= 1
coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear")
coords_2 = coords_2.to(torch.int64)
return ratios, coords_1, coords_2
n,c,h,w = samples.shape
h_new, w_new = (height, width)
#linear w
ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new)
coords_1 = coords_1.expand((n, c, h, -1))
coords_2 = coords_2.expand((n, c, h, -1))
ratios = ratios.expand((n, 1, h, -1))
pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c))
pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c))
ratios = ratios.movedim(1, -1).reshape((-1,1))
result = slerp(pass_1, pass_2, ratios)
result = result.reshape(n, h, w_new, c).movedim(-1, 1)
#linear h
ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new)
coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new))
pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c))
pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c))
ratios = ratios.movedim(1, -1).reshape((-1,1))
result = slerp(pass_1, pass_2, ratios)
result = result.reshape(n, h_new, w_new, c).movedim(-1, 1)
return result
def common_upscale(samples, width, height, upscale_method, crop):
if crop == "center":
old_width = samples.shape[3]
@ -61,7 +148,11 @@ def common_upscale(samples, width, height, upscale_method, crop):
s = samples[:,:,y:old_height-y,x:old_width-x]
else:
s = samples
return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
if upscale_method == "bislerp":
return bislerp(s, width, height)
else:
return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))

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import math
import torch.nn as nn
class CA_layer(nn.Module):
def __init__(self, channel, reduction=16):
super(CA_layer, self).__init__()
# global average pooling
self.gap = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Conv2d(channel, channel // reduction, kernel_size=(1, 1), bias=False),
nn.GELU(),
nn.Conv2d(channel // reduction, channel, kernel_size=(1, 1), bias=False),
# nn.Sigmoid()
)
def forward(self, x):
y = self.fc(self.gap(x))
return x * y.expand_as(x)
class Simple_CA_layer(nn.Module):
def __init__(self, channel):
super(Simple_CA_layer, self).__init__()
self.gap = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Conv2d(
in_channels=channel,
out_channels=channel,
kernel_size=1,
padding=0,
stride=1,
groups=1,
bias=True,
)
def forward(self, x):
return x * self.fc(self.gap(x))
class ECA_layer(nn.Module):
"""Constructs a ECA module.
Args:
channel: Number of channels of the input feature map
k_size: Adaptive selection of kernel size
"""
def __init__(self, channel):
super(ECA_layer, self).__init__()
b = 1
gamma = 2
k_size = int(abs(math.log(channel, 2) + b) / gamma)
k_size = k_size if k_size % 2 else k_size + 1
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv = nn.Conv1d(
1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False
)
# self.sigmoid = nn.Sigmoid()
def forward(self, x):
# x: input features with shape [b, c, h, w]
# b, c, h, w = x.size()
# feature descriptor on the global spatial information
y = self.avg_pool(x)
# Two different branches of ECA module
y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
# Multi-scale information fusion
# y = self.sigmoid(y)
return x * y.expand_as(x)
class ECA_MaxPool_layer(nn.Module):
"""Constructs a ECA module.
Args:
channel: Number of channels of the input feature map
k_size: Adaptive selection of kernel size
"""
def __init__(self, channel):
super(ECA_MaxPool_layer, self).__init__()
b = 1
gamma = 2
k_size = int(abs(math.log(channel, 2) + b) / gamma)
k_size = k_size if k_size % 2 else k_size + 1
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.conv = nn.Conv1d(
1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False
)
# self.sigmoid = nn.Sigmoid()
def forward(self, x):
# x: input features with shape [b, c, h, w]
# b, c, h, w = x.size()
# feature descriptor on the global spatial information
y = self.max_pool(x)
# Two different branches of ECA module
y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
# Multi-scale information fusion
# y = self.sigmoid(y)
return x * y.expand_as(x)

201
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@ -0,0 +1,201 @@
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577
comfy_extras/chainner_models/architecture/OmniSR/OSA.py Normal file
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@ -0,0 +1,577 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: OSA.py
# Created Date: Tuesday April 28th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Sunday, 23rd April 2023 3:07:42 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2020 Shanghai Jiao Tong University
#############################################################
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from einops.layers.torch import Rearrange, Reduce
from torch import einsum, nn
from .layernorm import LayerNorm2d
# helpers
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def cast_tuple(val, length=1):
return val if isinstance(val, tuple) else ((val,) * length)
# helper classes
class PreNormResidual(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.norm = nn.LayerNorm(dim)
self.fn = fn
def forward(self, x):
return self.fn(self.norm(x)) + x
class Conv_PreNormResidual(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.norm = LayerNorm2d(dim)
self.fn = fn
def forward(self, x):
return self.fn(self.norm(x)) + x
class FeedForward(nn.Module):
def __init__(self, dim, mult=2, dropout=0.0):
super().__init__()
inner_dim = int(dim * mult)
self.net = nn.Sequential(
nn.Linear(dim, inner_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(inner_dim, dim),
nn.Dropout(dropout),
)
def forward(self, x):
return self.net(x)
class Conv_FeedForward(nn.Module):
def __init__(self, dim, mult=2, dropout=0.0):
super().__init__()
inner_dim = int(dim * mult)
self.net = nn.Sequential(
nn.Conv2d(dim, inner_dim, 1, 1, 0),
nn.GELU(),
nn.Dropout(dropout),
nn.Conv2d(inner_dim, dim, 1, 1, 0),
nn.Dropout(dropout),
)
def forward(self, x):
return self.net(x)
class Gated_Conv_FeedForward(nn.Module):
def __init__(self, dim, mult=1, bias=False, dropout=0.0):
super().__init__()
hidden_features = int(dim * mult)
self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias)
self.dwconv = nn.Conv2d(
hidden_features * 2,
hidden_features * 2,
kernel_size=3,
stride=1,
padding=1,
groups=hidden_features * 2,
bias=bias,
)
self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias)
def forward(self, x):
x = self.project_in(x)
x1, x2 = self.dwconv(x).chunk(2, dim=1)
x = F.gelu(x1) * x2
x = self.project_out(x)
return x
# MBConv
class SqueezeExcitation(nn.Module):
def __init__(self, dim, shrinkage_rate=0.25):
super().__init__()
hidden_dim = int(dim * shrinkage_rate)
self.gate = nn.Sequential(
Reduce("b c h w -> b c", "mean"),
nn.Linear(dim, hidden_dim, bias=False),
nn.SiLU(),
nn.Linear(hidden_dim, dim, bias=False),
nn.Sigmoid(),
Rearrange("b c -> b c 1 1"),
)
def forward(self, x):
return x * self.gate(x)
class MBConvResidual(nn.Module):
def __init__(self, fn, dropout=0.0):
super().__init__()
self.fn = fn
self.dropsample = Dropsample(dropout)
def forward(self, x):
out = self.fn(x)
out = self.dropsample(out)
return out + x
class Dropsample(nn.Module):
def __init__(self, prob=0):
super().__init__()
self.prob = prob
def forward(self, x):
device = x.device
if self.prob == 0.0 or (not self.training):
return x
keep_mask = (
torch.FloatTensor((x.shape[0], 1, 1, 1), device=device).uniform_()
> self.prob
)
return x * keep_mask / (1 - self.prob)
def MBConv(
dim_in, dim_out, *, downsample, expansion_rate=4, shrinkage_rate=0.25, dropout=0.0
):
hidden_dim = int(expansion_rate * dim_out)
stride = 2 if downsample else 1
net = nn.Sequential(
nn.Conv2d(dim_in, hidden_dim, 1),
# nn.BatchNorm2d(hidden_dim),
nn.GELU(),
nn.Conv2d(
hidden_dim, hidden_dim, 3, stride=stride, padding=1, groups=hidden_dim
),
# nn.BatchNorm2d(hidden_dim),
nn.GELU(),
SqueezeExcitation(hidden_dim, shrinkage_rate=shrinkage_rate),
nn.Conv2d(hidden_dim, dim_out, 1),
# nn.BatchNorm2d(dim_out)
)
if dim_in == dim_out and not downsample:
net = MBConvResidual(net, dropout=dropout)
return net
# attention related classes
class Attention(nn.Module):
def __init__(
self,
dim,
dim_head=32,
dropout=0.0,
window_size=7,
with_pe=True,
):
super().__init__()
assert (
dim % dim_head
) == 0, "dimension should be divisible by dimension per head"
self.heads = dim // dim_head
self.scale = dim_head**-0.5
self.with_pe = with_pe
self.to_qkv = nn.Linear(dim, dim * 3, bias=False)
self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout))
self.to_out = nn.Sequential(
nn.Linear(dim, dim, bias=False), nn.Dropout(dropout)
)
# relative positional bias
if self.with_pe:
self.rel_pos_bias = nn.Embedding((2 * window_size - 1) ** 2, self.heads)
pos = torch.arange(window_size)
grid = torch.stack(torch.meshgrid(pos, pos))
grid = rearrange(grid, "c i j -> (i j) c")
rel_pos = rearrange(grid, "i ... -> i 1 ...") - rearrange(
grid, "j ... -> 1 j ..."
)
rel_pos += window_size - 1
rel_pos_indices = (rel_pos * torch.tensor([2 * window_size - 1, 1])).sum(
dim=-1
)
self.register_buffer("rel_pos_indices", rel_pos_indices, persistent=False)
def forward(self, x):
batch, height, width, window_height, window_width, _, device, h = (
*x.shape,
x.device,
self.heads,
)
# flatten
x = rearrange(x, "b x y w1 w2 d -> (b x y) (w1 w2) d")
# project for queries, keys, values
q, k, v = self.to_qkv(x).chunk(3, dim=-1)
# split heads
q, k, v = map(lambda t: rearrange(t, "b n (h d ) -> b h n d", h=h), (q, k, v))
# scale
q = q * self.scale
# sim
sim = einsum("b h i d, b h j d -> b h i j", q, k)
# add positional bias
if self.with_pe:
bias = self.rel_pos_bias(self.rel_pos_indices)
sim = sim + rearrange(bias, "i j h -> h i j")
# attention
attn = self.attend(sim)
# aggregate
out = einsum("b h i j, b h j d -> b h i d", attn, v)
# merge heads
out = rearrange(
out, "b h (w1 w2) d -> b w1 w2 (h d)", w1=window_height, w2=window_width
)
# combine heads out
out = self.to_out(out)
return rearrange(out, "(b x y) ... -> b x y ...", x=height, y=width)
class Block_Attention(nn.Module):
def __init__(
self,
dim,
dim_head=32,
bias=False,
dropout=0.0,
window_size=7,
with_pe=True,
):
super().__init__()
assert (
dim % dim_head
) == 0, "dimension should be divisible by dimension per head"
self.heads = dim // dim_head
self.ps = window_size
self.scale = dim_head**-0.5
self.with_pe = with_pe
self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
self.qkv_dwconv = nn.Conv2d(
dim * 3,
dim * 3,
kernel_size=3,
stride=1,
padding=1,
groups=dim * 3,
bias=bias,
)
self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout))
self.to_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
def forward(self, x):
# project for queries, keys, values
b, c, h, w = x.shape
qkv = self.qkv_dwconv(self.qkv(x))
q, k, v = qkv.chunk(3, dim=1)
# split heads
q, k, v = map(
lambda t: rearrange(
t,
"b (h d) (x w1) (y w2) -> (b x y) h (w1 w2) d",
h=self.heads,
w1=self.ps,
w2=self.ps,
),
(q, k, v),
)
# scale
q = q * self.scale
# sim
sim = einsum("b h i d, b h j d -> b h i j", q, k)
# attention
attn = self.attend(sim)
# aggregate
out = einsum("b h i j, b h j d -> b h i d", attn, v)
# merge heads
out = rearrange(
out,
"(b x y) head (w1 w2) d -> b (head d) (x w1) (y w2)",
x=h // self.ps,
y=w // self.ps,
head=self.heads,
w1=self.ps,
w2=self.ps,
)
out = self.to_out(out)
return out
class Channel_Attention(nn.Module):
def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7):
super(Channel_Attention, self).__init__()
self.heads = heads
self.temperature = nn.Parameter(torch.ones(heads, 1, 1))
self.ps = window_size
self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
self.qkv_dwconv = nn.Conv2d(
dim * 3,
dim * 3,
kernel_size=3,
stride=1,
padding=1,
groups=dim * 3,
bias=bias,
)
self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
def forward(self, x):
b, c, h, w = x.shape
qkv = self.qkv_dwconv(self.qkv(x))
qkv = qkv.chunk(3, dim=1)
q, k, v = map(
lambda t: rearrange(
t,
"b (head d) (h ph) (w pw) -> b (h w) head d (ph pw)",
ph=self.ps,
pw=self.ps,
head=self.heads,
),
qkv,
)
q = F.normalize(q, dim=-1)
k = F.normalize(k, dim=-1)
attn = (q @ k.transpose(-2, -1)) * self.temperature
attn = attn.softmax(dim=-1)
out = attn @ v
out = rearrange(
out,
"b (h w) head d (ph pw) -> b (head d) (h ph) (w pw)",
h=h // self.ps,
w=w // self.ps,
ph=self.ps,
pw=self.ps,
head=self.heads,
)
out = self.project_out(out)
return out
class Channel_Attention_grid(nn.Module):
def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7):
super(Channel_Attention_grid, self).__init__()
self.heads = heads
self.temperature = nn.Parameter(torch.ones(heads, 1, 1))
self.ps = window_size
self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
self.qkv_dwconv = nn.Conv2d(
dim * 3,
dim * 3,
kernel_size=3,
stride=1,
padding=1,
groups=dim * 3,
bias=bias,
)
self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
def forward(self, x):
b, c, h, w = x.shape
qkv = self.qkv_dwconv(self.qkv(x))
qkv = qkv.chunk(3, dim=1)
q, k, v = map(
lambda t: rearrange(
t,
"b (head d) (h ph) (w pw) -> b (ph pw) head d (h w)",
ph=self.ps,
pw=self.ps,
head=self.heads,
),
qkv,
)
q = F.normalize(q, dim=-1)
k = F.normalize(k, dim=-1)
attn = (q @ k.transpose(-2, -1)) * self.temperature
attn = attn.softmax(dim=-1)
out = attn @ v
out = rearrange(
out,
"b (ph pw) head d (h w) -> b (head d) (h ph) (w pw)",
h=h // self.ps,
w=w // self.ps,
ph=self.ps,
pw=self.ps,
head=self.heads,
)
out = self.project_out(out)
return out
class OSA_Block(nn.Module):
def __init__(
self,
channel_num=64,
bias=True,
ffn_bias=True,
window_size=8,
with_pe=False,
dropout=0.0,
):
super(OSA_Block, self).__init__()
w = window_size
self.layer = nn.Sequential(
MBConv(
channel_num,
channel_num,
downsample=False,
expansion_rate=1,
shrinkage_rate=0.25,
),
Rearrange(
"b d (x w1) (y w2) -> b x y w1 w2 d", w1=w, w2=w
), # block-like attention
PreNormResidual(
channel_num,
Attention(
dim=channel_num,
dim_head=channel_num // 4,
dropout=dropout,
window_size=window_size,
with_pe=with_pe,
),
),
Rearrange("b x y w1 w2 d -> b d (x w1) (y w2)"),
Conv_PreNormResidual(
channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)
),
# channel-like attention
Conv_PreNormResidual(
channel_num,
Channel_Attention(
dim=channel_num, heads=4, dropout=dropout, window_size=window_size
),
),
Conv_PreNormResidual(
channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)
),
Rearrange(
"b d (w1 x) (w2 y) -> b x y w1 w2 d", w1=w, w2=w
), # grid-like attention
PreNormResidual(
channel_num,
Attention(
dim=channel_num,
dim_head=channel_num // 4,
dropout=dropout,
window_size=window_size,
with_pe=with_pe,
),
),
Rearrange("b x y w1 w2 d -> b d (w1 x) (w2 y)"),
Conv_PreNormResidual(
channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)
),
# channel-like attention
Conv_PreNormResidual(
channel_num,
Channel_Attention_grid(
dim=channel_num, heads=4, dropout=dropout, window_size=window_size
),
),
Conv_PreNormResidual(
channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)
),
)
def forward(self, x):
out = self.layer(x)
return out

60
comfy_extras/chainner_models/architecture/OmniSR/OSAG.py Normal file
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@ -0,0 +1,60 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: OSAG.py
# Created Date: Tuesday April 28th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Sunday, 23rd April 2023 3:08:49 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2020 Shanghai Jiao Tong University
#############################################################
import torch.nn as nn
from .esa import ESA
from .OSA import OSA_Block
class OSAG(nn.Module):
def __init__(
self,
channel_num=64,
bias=True,
block_num=4,
ffn_bias=False,
window_size=0,
pe=False,
):
super(OSAG, self).__init__()
# print("window_size: %d" % (window_size))
# print("with_pe", pe)
# print("ffn_bias: %d" % (ffn_bias))
# block_script_name = kwargs.get("block_script_name", "OSA")
# block_class_name = kwargs.get("block_class_name", "OSA_Block")
# script_name = "." + block_script_name
# package = __import__(script_name, fromlist=True)
block_class = OSA_Block # getattr(package, block_class_name)
group_list = []
for _ in range(block_num):
temp_res = block_class(
channel_num,
bias,
ffn_bias=ffn_bias,
window_size=window_size,
with_pe=pe,
)
group_list.append(temp_res)
group_list.append(nn.Conv2d(channel_num, channel_num, 1, 1, 0, bias=bias))
self.residual_layer = nn.Sequential(*group_list)
esa_channel = max(channel_num // 4, 16)
self.esa = ESA(esa_channel, channel_num)
def forward(self, x):
out = self.residual_layer(x)
out = out + x
return self.esa(out)

133
comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py Normal file
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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: OmniSR.py
# Created Date: Tuesday April 28th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Sunday, 23rd April 2023 3:06:36 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2020 Shanghai Jiao Tong University
#############################################################
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from .OSAG import OSAG
from .pixelshuffle import pixelshuffle_block
class OmniSR(nn.Module):
def __init__(
self,
state_dict,
**kwargs,
):
super(OmniSR, self).__init__()
self.state = state_dict
bias = True # Fine to assume this for now
block_num = 1 # Fine to assume this for now
ffn_bias = True
pe = True
num_feat = state_dict["input.weight"].shape[0] or 64
num_in_ch = state_dict["input.weight"].shape[1] or 3
num_out_ch = num_in_ch # we can just assume this for now. pixelshuffle smh
pixelshuffle_shape = state_dict["up.0.weight"].shape[0]
up_scale = math.sqrt(pixelshuffle_shape / num_out_ch)
if up_scale - int(up_scale) > 0:
print(
"out_nc is probably different than in_nc, scale calculation might be wrong"
)
up_scale = int(up_scale)
res_num = 0
for key in state_dict.keys():
if "residual_layer" in key:
temp_res_num = int(key.split(".")[1])
if temp_res_num > res_num:
res_num = temp_res_num
res_num = res_num + 1 # zero-indexed
residual_layer = []
self.res_num = res_num
self.window_size = 8 # we can just assume this for now, but there's probably a way to calculate it (just need to get the sqrt of the right layer)
self.up_scale = up_scale
for _ in range(res_num):
temp_res = OSAG(
channel_num=num_feat,
bias=bias,
block_num=block_num,
ffn_bias=ffn_bias,
window_size=self.window_size,
pe=pe,
)
residual_layer.append(temp_res)
self.residual_layer = nn.Sequential(*residual_layer)
self.input = nn.Conv2d(
in_channels=num_in_ch,
out_channels=num_feat,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
)
self.output = nn.Conv2d(
in_channels=num_feat,
out_channels=num_feat,
kernel_size=3,
stride=1,
padding=1,
bias=bias,
)
self.up = pixelshuffle_block(num_feat, num_out_ch, up_scale, bias=bias)
# self.tail = pixelshuffle_block(num_feat,num_out_ch,up_scale,bias=bias)
# for m in self.modules():
# if isinstance(m, nn.Conv2d):
# n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
# m.weight.data.normal_(0, sqrt(2. / n))
# chaiNNer specific stuff
self.model_arch = "OmniSR"
self.sub_type = "SR"
self.in_nc = num_in_ch
self.out_nc = num_out_ch
self.num_feat = num_feat
self.scale = up_scale
self.supports_fp16 = True # TODO: Test this
self.supports_bfp16 = True
self.min_size_restriction = 16
self.load_state_dict(state_dict, strict=False)
def check_image_size(self, x):
_, _, h, w = x.size()
# import pdb; pdb.set_trace()
mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
# x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "constant", 0)
return x
def forward(self, x):
H, W = x.shape[2:]
x = self.check_image_size(x)
residual = self.input(x)
out = self.residual_layer(residual)
# origin
out = torch.add(self.output(out), residual)
out = self.up(out)
out = out[:, :, : H * self.up_scale, : W * self.up_scale]
return out

294
comfy_extras/chainner_models/architecture/OmniSR/esa.py Normal file
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@ -0,0 +1,294 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: esa.py
# Created Date: Tuesday April 28th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Thursday, 20th April 2023 9:28:06 am
# Modified By: Chen Xuanhong
# Copyright (c) 2020 Shanghai Jiao Tong University
#############################################################
import torch
import torch.nn as nn
import torch.nn.functional as F
from .layernorm import LayerNorm2d
def moment(x, dim=(2, 3), k=2):
assert len(x.size()) == 4
mean = torch.mean(x, dim=dim).unsqueeze(-1).unsqueeze(-1)
mk = (1 / (x.size(2) * x.size(3))) * torch.sum(torch.pow(x - mean, k), dim=dim)
return mk
class ESA(nn.Module):
"""
Modification of Enhanced Spatial Attention (ESA), which is proposed by
`Residual Feature Aggregation Network for Image Super-Resolution`
Note: `conv_max` and `conv3_` are NOT used here, so the corresponding codes
are deleted.
"""
def __init__(self, esa_channels, n_feats, conv=nn.Conv2d):
super(ESA, self).__init__()
f = esa_channels
self.conv1 = conv(n_feats, f, kernel_size=1)
self.conv_f = conv(f, f, kernel_size=1)
self.conv2 = conv(f, f, kernel_size=3, stride=2, padding=0)
self.conv3 = conv(f, f, kernel_size=3, padding=1)
self.conv4 = conv(f, n_feats, kernel_size=1)
self.sigmoid = nn.Sigmoid()
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
c1_ = self.conv1(x)
c1 = self.conv2(c1_)
v_max = F.max_pool2d(c1, kernel_size=7, stride=3)
c3 = self.conv3(v_max)
c3 = F.interpolate(
c3, (x.size(2), x.size(3)), mode="bilinear", align_corners=False
)
cf = self.conv_f(c1_)
c4 = self.conv4(c3 + cf)
m = self.sigmoid(c4)
return x * m
class LK_ESA(nn.Module):
def __init__(
self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True
):
super(LK_ESA, self).__init__()
f = esa_channels
self.conv1 = conv(n_feats, f, kernel_size=1)
self.conv_f = conv(f, f, kernel_size=1)
kernel_size = 17
kernel_expand = kernel_expand
padding = kernel_size // 2
self.vec_conv = nn.Conv2d(
in_channels=f * kernel_expand,
out_channels=f * kernel_expand,
kernel_size=(1, kernel_size),
padding=(0, padding),
groups=2,
bias=bias,
)
self.vec_conv3x1 = nn.Conv2d(
in_channels=f * kernel_expand,
out_channels=f * kernel_expand,
kernel_size=(1, 3),
padding=(0, 1),
groups=2,
bias=bias,
)
self.hor_conv = nn.Conv2d(
in_channels=f * kernel_expand,
out_channels=f * kernel_expand,
kernel_size=(kernel_size, 1),
padding=(padding, 0),
groups=2,
bias=bias,
)
self.hor_conv1x3 = nn.Conv2d(
in_channels=f * kernel_expand,
out_channels=f * kernel_expand,
kernel_size=(3, 1),
padding=(1, 0),
groups=2,
bias=bias,
)
self.conv4 = conv(f, n_feats, kernel_size=1)
self.sigmoid = nn.Sigmoid()
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
c1_ = self.conv1(x)
res = self.vec_conv(c1_) + self.vec_conv3x1(c1_)
res = self.hor_conv(res) + self.hor_conv1x3(res)
cf = self.conv_f(c1_)
c4 = self.conv4(res + cf)
m = self.sigmoid(c4)
return x * m
class LK_ESA_LN(nn.Module):
def __init__(
self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True
):
super(LK_ESA_LN, self).__init__()
f = esa_channels
self.conv1 = conv(n_feats, f, kernel_size=1)
self.conv_f = conv(f, f, kernel_size=1)
kernel_size = 17
kernel_expand = kernel_expand
padding = kernel_size // 2
self.norm = LayerNorm2d(n_feats)
self.vec_conv = nn.Conv2d(
in_channels=f * kernel_expand,
out_channels=f * kernel_expand,
kernel_size=(1, kernel_size),
padding=(0, padding),
groups=2,
bias=bias,
)
self.vec_conv3x1 = nn.Conv2d(
in_channels=f * kernel_expand,
out_channels=f * kernel_expand,
kernel_size=(1, 3),
padding=(0, 1),
groups=2,
bias=bias,
)
self.hor_conv = nn.Conv2d(
in_channels=f * kernel_expand,
out_channels=f * kernel_expand,
kernel_size=(kernel_size, 1),
padding=(padding, 0),
groups=2,
bias=bias,
)
self.hor_conv1x3 = nn.Conv2d(
in_channels=f * kernel_expand,
out_channels=f * kernel_expand,
kernel_size=(3, 1),
padding=(1, 0),
groups=2,
bias=bias,
)
self.conv4 = conv(f, n_feats, kernel_size=1)
self.sigmoid = nn.Sigmoid()
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
c1_ = self.norm(x)
c1_ = self.conv1(c1_)
res = self.vec_conv(c1_) + self.vec_conv3x1(c1_)
res = self.hor_conv(res) + self.hor_conv1x3(res)
cf = self.conv_f(c1_)
c4 = self.conv4(res + cf)
m = self.sigmoid(c4)
return x * m
class AdaGuidedFilter(nn.Module):
def __init__(
self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True
):
super(AdaGuidedFilter, self).__init__()
self.gap = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Conv2d(
in_channels=n_feats,
out_channels=1,
kernel_size=1,
padding=0,
stride=1,
groups=1,
bias=True,
)
self.r = 5
def box_filter(self, x, r):
channel = x.shape[1]
kernel_size = 2 * r + 1
weight = 1.0 / (kernel_size**2)
box_kernel = weight * torch.ones(
(channel, 1, kernel_size, kernel_size), dtype=torch.float32, device=x.device
)
output = F.conv2d(x, weight=box_kernel, stride=1, padding=r, groups=channel)
return output
def forward(self, x):
_, _, H, W = x.shape
N = self.box_filter(
torch.ones((1, 1, H, W), dtype=x.dtype, device=x.device), self.r
)
# epsilon = self.fc(self.gap(x))
# epsilon = torch.pow(epsilon, 2)
epsilon = 1e-2
mean_x = self.box_filter(x, self.r) / N
var_x = self.box_filter(x * x, self.r) / N - mean_x * mean_x
A = var_x / (var_x + epsilon)
b = (1 - A) * mean_x
m = A * x + b
# mean_A = self.box_filter(A, self.r) / N
# mean_b = self.box_filter(b, self.r) / N
# m = mean_A * x + mean_b
return x * m
class AdaConvGuidedFilter(nn.Module):
def __init__(
self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True
):
super(AdaConvGuidedFilter, self).__init__()
f = esa_channels
self.conv_f = conv(f, f, kernel_size=1)
kernel_size = 17
kernel_expand = kernel_expand
padding = kernel_size // 2
self.vec_conv = nn.Conv2d(
in_channels=f,
out_channels=f,
kernel_size=(1, kernel_size),
padding=(0, padding),
groups=f,
bias=bias,
)
self.hor_conv = nn.Conv2d(
in_channels=f,
out_channels=f,
kernel_size=(kernel_size, 1),
padding=(padding, 0),
groups=f,
bias=bias,
)
self.gap = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Conv2d(
in_channels=f,
out_channels=f,
kernel_size=1,
padding=0,
stride=1,
groups=1,
bias=True,
)
def forward(self, x):
y = self.vec_conv(x)
y = self.hor_conv(y)
sigma = torch.pow(y, 2)
epsilon = self.fc(self.gap(y))
weight = sigma / (sigma + epsilon)
m = weight * x + (1 - weight)
return x * m

70
comfy_extras/chainner_models/architecture/OmniSR/layernorm.py Normal file
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@ -0,0 +1,70 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: layernorm.py
# Created Date: Tuesday April 28th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Thursday, 20th April 2023 9:28:20 am
# Modified By: Chen Xuanhong
# Copyright (c) 2020 Shanghai Jiao Tong University
#############################################################
import torch
import torch.nn as nn
class LayerNormFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, x, weight, bias, eps):
ctx.eps = eps
N, C, H, W = x.size()
mu = x.mean(1, keepdim=True)
var = (x - mu).pow(2).mean(1, keepdim=True)
y = (x - mu) / (var + eps).sqrt()
ctx.save_for_backward(y, var, weight)
y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1)
return y
@staticmethod
def backward(ctx, grad_output):
eps = ctx.eps
N, C, H, W = grad_output.size()
y, var, weight = ctx.saved_variables
g = grad_output * weight.view(1, C, 1, 1)
mean_g = g.mean(dim=1, keepdim=True)
mean_gy = (g * y).mean(dim=1, keepdim=True)
gx = 1.0 / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g)
return (
gx,
(grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0),
grad_output.sum(dim=3).sum(dim=2).sum(dim=0),
None,
)
class LayerNorm2d(nn.Module):
def __init__(self, channels, eps=1e-6):
super(LayerNorm2d, self).__init__()
self.register_parameter("weight", nn.Parameter(torch.ones(channels)))
self.register_parameter("bias", nn.Parameter(torch.zeros(channels)))
self.eps = eps
def forward(self, x):
return LayerNormFunction.apply(x, self.weight, self.bias, self.eps)
class GRN(nn.Module):
"""GRN (Global Response Normalization) layer"""
def __init__(self, dim):
super().__init__()
self.gamma = nn.Parameter(torch.zeros(1, dim, 1, 1))
self.beta = nn.Parameter(torch.zeros(1, dim, 1, 1))
def forward(self, x):
Gx = torch.norm(x, p=2, dim=(2, 3), keepdim=True)
Nx = Gx / (Gx.mean(dim=1, keepdim=True) + 1e-6)
return self.gamma * (x * Nx) + self.beta + x

31
comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py Normal file
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@ -0,0 +1,31 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: pixelshuffle.py
# Created Date: Friday July 1st 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Friday, 1st July 2022 10:18:39 am
# Modified By: Chen Xuanhong
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
import torch.nn as nn
def pixelshuffle_block(
in_channels, out_channels, upscale_factor=2, kernel_size=3, bias=False
):
"""
Upsample features according to `upscale_factor`.
"""
padding = kernel_size // 2
conv = nn.Conv2d(
in_channels,
out_channels * (upscale_factor**2),
kernel_size,
padding=1,
bias=bias,
)
pixel_shuffle = nn.PixelShuffle(upscale_factor)
return nn.Sequential(*[conv, pixel_shuffle])

17
comfy_extras/chainner_models/architecture/RRDB.py
View File

@ -79,6 +79,12 @@ class RRDBNet(nn.Module):
self.scale: int = self.get_scale()
self.num_filters: int = self.state[self.key_arr[0]].shape[0]
c2x2 = False
if self.state["model.0.weight"].shape[-2] == 2:
c2x2 = True
self.scale = round(math.sqrt(self.scale / 4))
self.model_arch = "ESRGAN-2c2"
self.supports_fp16 = True
self.supports_bfp16 = True
self.min_size_restriction = None
@ -105,11 +111,15 @@ class RRDBNet(nn.Module):
out_nc=self.num_filters,
upscale_factor=3,
act_type=self.act,
c2x2=c2x2,
)
else:
upsample_blocks = [
upsample_block(
in_nc=self.num_filters, out_nc=self.num_filters, act_type=self.act
in_nc=self.num_filters,
out_nc=self.num_filters,
act_type=self.act,
c2x2=c2x2,
)
for _ in range(int(math.log(self.scale, 2)))
]
@ -122,6 +132,7 @@ class RRDBNet(nn.Module):
kernel_size=3,
norm_type=None,
act_type=None,
c2x2=c2x2,
),
B.ShortcutBlock(
B.sequential(
@ -138,6 +149,7 @@ class RRDBNet(nn.Module):
act_type=self.act,
mode="CNA",
plus=self.plus,
c2x2=c2x2,
)
for _ in range(self.num_blocks)
],
@ -149,6 +161,7 @@ class RRDBNet(nn.Module):
norm_type=self.norm,
act_type=None,
mode=self.mode,
c2x2=c2x2,
),
)
),
@ -160,6 +173,7 @@ class RRDBNet(nn.Module):
kernel_size=3,
norm_type=None,
act_type=self.act,
c2x2=c2x2,
),
# hr_conv1
B.conv_block(
@ -168,6 +182,7 @@ class RRDBNet(nn.Module):
kernel_size=3,
norm_type=None,
act_type=None,
c2x2=c2x2,
),
)

30
comfy_extras/chainner_models/architecture/block.py
View File

@ -141,6 +141,19 @@ def sequential(*args):
ConvMode = Literal["CNA", "NAC", "CNAC"]
# 2x2x2 Conv Block
def conv_block_2c2(
in_nc,
out_nc,
act_type="relu",
):
return sequential(
nn.Conv2d(in_nc, out_nc, kernel_size=2, padding=1),
nn.Conv2d(out_nc, out_nc, kernel_size=2, padding=0),
act(act_type) if act_type else None,
)
def conv_block(
in_nc: int,
out_nc: int,
@ -153,12 +166,17 @@ def conv_block(
norm_type: str | None = None,
act_type: str | None = "relu",
mode: ConvMode = "CNA",
c2x2=False,
):
"""
Conv layer with padding, normalization, activation
mode: CNA --> Conv -> Norm -> Act
NAC --> Norm -> Act --> Conv (Identity Mappings in Deep Residual Networks, ECCV16)
"""
if c2x2:
return conv_block_2c2(in_nc, out_nc, act_type=act_type)
assert mode in ("CNA", "NAC", "CNAC"), "Wrong conv mode [{:s}]".format(mode)
padding = get_valid_padding(kernel_size, dilation)
p = pad(pad_type, padding) if pad_type and pad_type != "zero" else None
@ -285,6 +303,7 @@ class RRDB(nn.Module):
_convtype="Conv2D",
_spectral_norm=False,
plus=False,
c2x2=False,
):
super(RRDB, self).__init__()
self.RDB1 = ResidualDenseBlock_5C(
@ -298,6 +317,7 @@ class RRDB(nn.Module):
act_type,
mode,
plus=plus,
c2x2=c2x2,
)
self.RDB2 = ResidualDenseBlock_5C(
nf,
@ -310,6 +330,7 @@ class RRDB(nn.Module):
act_type,
mode,
plus=plus,
c2x2=c2x2,
)
self.RDB3 = ResidualDenseBlock_5C(
nf,
@ -322,6 +343,7 @@ class RRDB(nn.Module):
act_type,
mode,
plus=plus,
c2x2=c2x2,
)
def forward(self, x):
@ -365,6 +387,7 @@ class ResidualDenseBlock_5C(nn.Module):
act_type="leakyrelu",
mode: ConvMode = "CNA",
plus=False,
c2x2=False,
):
super(ResidualDenseBlock_5C, self).__init__()
@ -382,6 +405,7 @@ class ResidualDenseBlock_5C(nn.Module):
norm_type=norm_type,
act_type=act_type,
mode=mode,
c2x2=c2x2,
)
self.conv2 = conv_block(
nf + gc,
@ -393,6 +417,7 @@ class ResidualDenseBlock_5C(nn.Module):
norm_type=norm_type,
act_type=act_type,
mode=mode,
c2x2=c2x2,
)
self.conv3 = conv_block(
nf + 2 * gc,
@ -404,6 +429,7 @@ class ResidualDenseBlock_5C(nn.Module):
norm_type=norm_type,
act_type=act_type,
mode=mode,
c2x2=c2x2,
)
self.conv4 = conv_block(
nf + 3 * gc,
@ -415,6 +441,7 @@ class ResidualDenseBlock_5C(nn.Module):
norm_type=norm_type,
act_type=act_type,
mode=mode,
c2x2=c2x2,
)
if mode == "CNA":
last_act = None
@ -430,6 +457,7 @@ class ResidualDenseBlock_5C(nn.Module):
norm_type=norm_type,
act_type=last_act,
mode=mode,
c2x2=c2x2,
)
def forward(self, x):
@ -499,6 +527,7 @@ def upconv_block(
norm_type: str | None = None,
act_type="relu",
mode="nearest",
c2x2=False,
):
# Up conv
# described in https://distill.pub/2016/deconv-checkerboard/
@ -512,5 +541,6 @@ def upconv_block(
pad_type=pad_type,
norm_type=norm_type,
act_type=act_type,
c2x2=c2x2,
)
return sequential(upsample, conv)

5
comfy_extras/chainner_models/model_loading.py
View File

@ -6,6 +6,7 @@ from .architecture.face.restoreformer_arch import RestoreFormer
from .architecture.HAT import HAT
from .architecture.LaMa import LaMa
from .architecture.MAT import MAT
from .architecture.OmniSR.OmniSR import OmniSR
from .architecture.RRDB import RRDBNet as ESRGAN
from .architecture.SPSR import SPSRNet as SPSR
from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2
@ -32,6 +33,7 @@ def load_state_dict(state_dict) -> PyTorchModel:
state_dict = state_dict["params"]
state_dict_keys = list(state_dict.keys())
# SRVGGNet Real-ESRGAN (v2)
if "body.0.weight" in state_dict_keys and "body.1.weight" in state_dict_keys:
model = RealESRGANv2(state_dict)
@ -79,6 +81,9 @@ def load_state_dict(state_dict) -> PyTorchModel:
# MAT
elif "synthesis.first_stage.conv_first.conv.resample_filter" in state_dict_keys:
model = MAT(state_dict)
# Omni-SR
elif "residual_layer.0.residual_layer.0.layer.0.fn.0.weight" in state_dict_keys:
model = OmniSR(state_dict)
# Regular ESRGAN, "new-arch" ESRGAN, Real-ESRGAN v1
else:
try:

4
comfy_extras/chainner_models/types.py
View File

@ -6,6 +6,7 @@ from .architecture.face.restoreformer_arch import RestoreFormer
from .architecture.HAT import HAT
from .architecture.LaMa import LaMa
from .architecture.MAT import MAT
from .architecture.OmniSR.OmniSR import OmniSR
from .architecture.RRDB import RRDBNet as ESRGAN
from .architecture.SPSR import SPSRNet as SPSR
from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2
@ -13,7 +14,7 @@ from .architecture.SwiftSRGAN import Generator as SwiftSRGAN
from .architecture.Swin2SR import Swin2SR
from .architecture.SwinIR import SwinIR
PyTorchSRModels = (RealESRGANv2, SPSR, SwiftSRGAN, ESRGAN, SwinIR, Swin2SR, HAT)
PyTorchSRModels = (RealESRGANv2, SPSR, SwiftSRGAN, ESRGAN, SwinIR, Swin2SR, HAT, OmniSR)
PyTorchSRModel = Union[
RealESRGANv2,
SPSR,
@ -22,6 +23,7 @@ PyTorchSRModel = Union[
SwinIR,
Swin2SR,
HAT,
OmniSR,
]

8
comfy_extras/nodes_mask.py
View File

@ -167,7 +167,7 @@ class MaskComposite:
"source": ("MASK",),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"operation": (["multiply", "add", "subtract"],),
"operation": (["multiply", "add", "subtract", "and", "or", "xor"],),
}
}
@ -193,6 +193,12 @@ class MaskComposite:
output[top:bottom, left:right] = destination_portion + source_portion
elif operation == "subtract":
output[top:bottom, left:right] = destination_portion - source_portion
elif operation == "and":
output[top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float()
elif operation == "or":
output[top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float()
elif operation == "xor":
output[top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float()
output = torch.clamp(output, 0.0, 1.0)

440
execution.py
View File

@ -102,13 +102,21 @@ def get_output_data(obj, input_data_all):
ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()}
return output, ui
def format_value(x):
if x is None:
return None
elif isinstance(x, (int, float, bool, str)):
return x
else:
return str(x)
def recursive_execute(server, prompt, outputs, current_item, extra_data, executed, prompt_id, outputs_ui):
unique_id = current_item
inputs = prompt[unique_id]['inputs']
class_type = prompt[unique_id]['class_type']
class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
if unique_id in outputs:
return
return (True, None, None)
for x in inputs:
input_data = inputs[x]
@ -117,22 +125,64 @@ def recursive_execute(server, prompt, outputs, current_item, extra_data, execute
input_unique_id = input_data[0]
output_index = input_data[1]
if input_unique_id not in outputs:
recursive_execute(server, prompt, outputs, input_unique_id, extra_data, executed, prompt_id, outputs_ui)
result = recursive_execute(server, prompt, outputs, input_unique_id, extra_data, executed, prompt_id, outputs_ui)
if result[0] is not True:
# Another node failed further upstream
return result
input_data_all = get_input_data(inputs, class_def, unique_id, outputs, prompt, extra_data)
if server.client_id is not None:
server.last_node_id = unique_id
server.send_sync("executing", { "node": unique_id, "prompt_id": prompt_id }, server.client_id)
obj = class_def()
output_data, output_ui = get_output_data(obj, input_data_all)
outputs[unique_id] = output_data
if len(output_ui) > 0:
outputs_ui[unique_id] = output_ui
input_data_all = None
try:
input_data_all = get_input_data(inputs, class_def, unique_id, outputs, prompt, extra_data)
if server.client_id is not None:
server.send_sync("executed", { "node": unique_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id)
server.last_node_id = unique_id
server.send_sync("executing", { "node": unique_id, "prompt_id": prompt_id }, server.client_id)
obj = class_def()
output_data, output_ui = get_output_data(obj, input_data_all)
outputs[unique_id] = output_data
if len(output_ui) > 0:
outputs_ui[unique_id] = output_ui
if server.client_id is not None:
server.send_sync("executed", { "node": unique_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id)
except comfy.model_management.InterruptProcessingException as iex:
print("Processing interrupted")
# skip formatting inputs/outputs
error_details = {
"node_id": unique_id,
}
return (False, error_details, iex)
except Exception as ex:
typ, _, tb = sys.exc_info()
exception_type = full_type_name(typ)
input_data_formatted = {}
if input_data_all is not None:
input_data_formatted = {}
for name, inputs in input_data_all.items():
input_data_formatted[name] = [format_value(x) for x in inputs]
output_data_formatted = {}
for node_id, node_outputs in outputs.items():
output_data_formatted[node_id] = [[format_value(x) for x in l] for l in node_outputs]
print("!!! Exception during processing !!!")
print(traceback.format_exc())
error_details = {
"node_id": unique_id,
"exception_message": str(ex),
"exception_type": exception_type,
"traceback": traceback.format_tb(tb),
"current_inputs": input_data_formatted,
"current_outputs": output_data_formatted
}
return (False, error_details, ex)
executed.add(unique_id)
return (True, None, None)
def recursive_will_execute(prompt, outputs, current_item):
unique_id = current_item
inputs = prompt[unique_id]['inputs']
@ -210,6 +260,48 @@ class PromptExecutor:
self.old_prompt = {}
self.server = server
def handle_execution_error(self, prompt_id, prompt, current_outputs, executed, error, ex):
node_id = error["node_id"]
class_type = prompt[node_id]["class_type"]
# First, send back the status to the frontend depending
# on the exception type
if isinstance(ex, comfy.model_management.InterruptProcessingException):
mes = {
"prompt_id": prompt_id,
"node_id": node_id,
"node_type": class_type,
"executed": list(executed),
}
self.server.send_sync("execution_interrupted", mes, self.server.client_id)
else:
if self.server.client_id is not None:
mes = {
"prompt_id": prompt_id,
"node_id": node_id,
"node_type": class_type,
"executed": list(executed),
"exception_message": error["exception_message"],
"exception_type": error["exception_type"],
"traceback": error["traceback"],
"current_inputs": error["current_inputs"],
"current_outputs": error["current_outputs"],
}
self.server.send_sync("execution_error", mes, self.server.client_id)
# Next, remove the subsequent outputs since they will not be executed
to_delete = []
for o in self.outputs:
if (o not in current_outputs) and (o not in executed):
to_delete += [o]
if o in self.old_prompt:
d = self.old_prompt.pop(o)
del d
for o in to_delete:
d = self.outputs.pop(o)
del d
def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
nodes.interrupt_processing(False)
@ -244,42 +336,30 @@ class PromptExecutor:
if self.server.client_id is not None:
self.server.send_sync("execution_cached", { "nodes": list(current_outputs) , "prompt_id": prompt_id}, self.server.client_id)
executed = set()
try:
to_execute = []
for x in list(execute_outputs):
to_execute += [(0, x)]
output_node_id = None
to_execute = []
while len(to_execute) > 0:
#always execute the output that depends on the least amount of unexecuted nodes first
to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1])), a[-1]), to_execute)))
x = to_execute.pop(0)[-1]
for node_id in list(execute_outputs):
to_execute += [(0, node_id)]
recursive_execute(self.server, prompt, self.outputs, x, extra_data, executed, prompt_id, self.outputs_ui)
except Exception as e:
if isinstance(e, comfy.model_management.InterruptProcessingException):
print("Processing interrupted")
else:
message = str(traceback.format_exc())
print(message)
if self.server.client_id is not None:
self.server.send_sync("execution_error", { "message": message, "prompt_id": prompt_id }, self.server.client_id)
while len(to_execute) > 0:
#always execute the output that depends on the least amount of unexecuted nodes first
to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1])), a[-1]), to_execute)))
output_node_id = to_execute.pop(0)[-1]
to_delete = []
for o in self.outputs:
if (o not in current_outputs) and (o not in executed):
to_delete += [o]
if o in self.old_prompt:
d = self.old_prompt.pop(o)
del d
for o in to_delete:
d = self.outputs.pop(o)
del d
finally:
for x in executed:
self.old_prompt[x] = copy.deepcopy(prompt[x])
self.server.last_node_id = None
if self.server.client_id is not None:
self.server.send_sync("executing", { "node": None, "prompt_id": prompt_id }, self.server.client_id)
# This call shouldn't raise anything if there's an error deep in
# the actual SD code, instead it will report the node where the
# error was raised
success, error, ex = recursive_execute(self.server, prompt, self.outputs, output_node_id, extra_data, executed, prompt_id, self.outputs_ui)
if success is not True:
self.handle_execution_error(prompt_id, prompt, current_outputs, executed, error, ex)
break
for x in executed:
self.old_prompt[x] = copy.deepcopy(prompt[x])
self.server.last_node_id = None
if self.server.client_id is not None:
self.server.send_sync("executing", { "node": None, "prompt_id": prompt_id }, self.server.client_id)
print("Prompt executed in {:.2f} seconds".format(time.perf_counter() - execution_start_time))
gc.collect()
@ -297,57 +377,202 @@ def validate_inputs(prompt, item, validated):
class_inputs = obj_class.INPUT_TYPES()
required_inputs = class_inputs['required']
errors = []
valid = True
for x in required_inputs:
if x not in inputs:
return (False, "Required input is missing. {}, {}".format(class_type, x))
error = {
"type": "required_input_missing",
"message": "Required input is missing",
"details": f"{x}",
"extra_info": {
"input_name": x
}
}
errors.append(error)
continue
val = inputs[x]
info = required_inputs[x]
type_input = info[0]
if isinstance(val, list):
if len(val) != 2:
return (False, "Bad Input. {}, {}".format(class_type, x))
error = {
"type": "bad_linked_input",
"message": "Bad linked input, must be a length-2 list of [node_id, slot_index]",
"details": f"{x}",
"extra_info": {
"input_name": x,
"input_config": info,
"received_value": val
}
}
errors.append(error)
continue
o_id = val[0]
o_class_type = prompt[o_id]['class_type']
r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES
if r[val[1]] != type_input:
return (False, "Return type mismatch. {}, {}, {} != {}".format(class_type, x, r[val[1]], type_input))
r = validate_inputs(prompt, o_id, validated)
if r[0] == False:
validated[o_id] = r
return r
received_type = r[val[1]]
details = f"{x}, {received_type} != {type_input}"
error = {
"type": "return_type_mismatch",
"message": "Return type mismatch between linked nodes",
"details": details,
"extra_info": {
"input_name": x,
"input_config": info,
"received_type": received_type,
"linked_node": val
}
}
errors.append(error)
continue
try:
r = validate_inputs(prompt, o_id, validated)
if r[0] is False:
# `r` will be set in `validated[o_id]` already
valid = False
continue
except Exception as ex:
typ, _, tb = sys.exc_info()
valid = False
exception_type = full_type_name(typ)
reasons = [{
"type": "exception_during_inner_validation",
"message": "Exception when validating inner node",
"details": str(ex),
"extra_info": {
"input_name": x,
"input_config": info,
"exception_message": str(ex),
"exception_type": exception_type,
"traceback": traceback.format_tb(tb),
"linked_node": val
}
}]
validated[o_id] = (False, reasons, o_id)
continue
else:
if type_input == "INT":
val = int(val)
inputs[x] = val
if type_input == "FLOAT":
val = float(val)
inputs[x] = val
if type_input == "STRING":
val = str(val)
inputs[x] = val
try:
if type_input == "INT":
val = int(val)
inputs[x] = val
if type_input == "FLOAT":
val = float(val)
inputs[x] = val
if type_input == "STRING":
val = str(val)
inputs[x] = val
except Exception as ex:
error = {
"type": "invalid_input_type",
"message": f"Failed to convert an input value to a {type_input} value",
"details": f"{x}, {val}, {ex}",
"extra_info": {
"input_name": x,
"input_config": info,
"received_value": val,
"exception_message": str(ex)
}
}
errors.append(error)
continue
if len(info) > 1:
if "min" in info[1] and val < info[1]["min"]:
return (False, "Value {} smaller than min of {}. {}, {}".format(val, info[1]["min"], class_type, x))
error = {
"type": "value_smaller_than_min",
"message": "Value {} smaller than min of {}".format(val, info[1]["min"]),
"details": f"{x}",
"extra_info": {
"input_name": x,
"input_config": info,
"received_value": val,
}
}
errors.append(error)
continue
if "max" in info[1] and val > info[1]["max"]:
return (False, "Value {} bigger than max of {}. {}, {}".format(val, info[1]["max"], class_type, x))
error = {
"type": "value_bigger_than_max",
"message": "Value {} bigger than max of {}".format(val, info[1]["max"]),
"details": f"{x}",
"extra_info": {
"input_name": x,
"input_config": info,
"received_value": val,
}
}
errors.append(error)
continue
if hasattr(obj_class, "VALIDATE_INPUTS"):
input_data_all = get_input_data(inputs, obj_class, unique_id)
#ret = obj_class.VALIDATE_INPUTS(**input_data_all)
ret = map_node_over_list(obj_class, input_data_all, "VALIDATE_INPUTS")
for r in ret:
if r != True:
return (False, "{}, {}".format(class_type, r))
for i, r in enumerate(ret):
if r is not True:
details = f"{x}"
if r is not False:
details += f" - {str(r)}"
error = {
"type": "custom_validation_failed",
"message": "Custom validation failed for node",
"details": details,
"extra_info": {
"input_name": x,
"input_config": info,
"received_value": val,
}
}
errors.append(error)
continue
else:
if isinstance(type_input, list):
if val not in type_input:
return (False, "Value not in list. {}, {}: {} not in {}".format(class_type, x, val, type_input))
input_config = info
list_info = ""
# Don't send back gigantic lists like if they're lots of
# scanned model filepaths
if len(type_input) > 20:
list_info = f"(list of length {len(type_input)})"
input_config = None
else:
list_info = str(type_input)
error = {
"type": "value_not_in_list",
"message": "Value not in list",
"details": f"{x}: '{val}' not in {list_info}",
"extra_info": {
"input_name": x,
"input_config": input_config,
"received_value": val,
}
}
errors.append(error)
continue
if len(errors) > 0 or valid is not True:
ret = (False, errors, unique_id)
else:
ret = (True, [], unique_id)
ret = (True, "")
validated[unique_id] = ret
return ret
def full_type_name(klass):
module = klass.__module__
if module == 'builtins':
return klass.__qualname__
return module + '.' + klass.__qualname__
def validate_prompt(prompt):
outputs = set()
for x in prompt:
@ -356,35 +581,86 @@ def validate_prompt(prompt):
outputs.add(x)
if len(outputs) == 0:
return (False, "Prompt has no outputs")
error = {
"type": "prompt_no_outputs",
"message": "Prompt has no outputs",
"details": "",
"extra_info": {}
}
return (False, error, [], [])
good_outputs = set()
errors = []
node_errors = {}
validated = {}
for o in outputs:
valid = False
reason = ""
reasons = []
try:
m = validate_inputs(prompt, o, validated)
valid = m[0]
reason = m[1]
except Exception as e:
print(traceback.format_exc())
reasons = m[1]
except Exception as ex:
typ, _, tb = sys.exc_info()
valid = False
reason = "Parsing error"
exception_type = full_type_name(typ)
reasons = [{
"type": "exception_during_validation",
"message": "Exception when validating node",
"details": str(ex),
"extra_info": {
"exception_type": exception_type,
"traceback": traceback.format_tb(tb)
}
}]
validated[o] = (False, reasons, o)
if valid == True:
if valid is True:
good_outputs.add(o)
else:
print("Failed to validate prompt for output {} {}".format(o, reason))
print("output will be ignored")
errors += [(o, reason)]
print(f"Failed to validate prompt for output {o}:")
if len(reasons) > 0:
print("* (prompt):")
for reason in reasons:
print(f" - {reason['message']}: {reason['details']}")
errors += [(o, reasons)]
for node_id, result in validated.items():
valid = result[0]
reasons = result[1]
# If a node upstream has errors, the nodes downstream will also
# be reported as invalid, but there will be no errors attached.
# So don't return those nodes as having errors in the response.
if valid is not True and len(reasons) > 0:
if node_id not in node_errors:
class_type = prompt[node_id]['class_type']
node_errors[node_id] = {
"errors": reasons,
"dependent_outputs": [],
"class_type": class_type
}
print(f"* {class_type} {node_id}:")
for reason in reasons:
print(f" - {reason['message']}: {reason['details']}")
node_errors[node_id]["dependent_outputs"].append(o)
print("Output will be ignored")
if len(good_outputs) == 0:
errors_list = "\n".join(set(map(lambda a: "{}".format(a[1]), errors)))
return (False, "Prompt has no properly connected outputs\n {}".format(errors_list))
errors_list = []
for o, errors in errors:
for error in errors:
errors_list.append(f"{error['message']}: {error['details']}")
errors_list = "\n".join(errors_list)
return (True, "", list(good_outputs))
error = {
"type": "prompt_outputs_failed_validation",
"message": "Prompt outputs failed validation",
"details": errors_list,
"extra_info": {}
}
return (False, error, list(good_outputs), node_errors)
return (True, None, list(good_outputs), node_errors)
class PromptQueue:

62
folder_paths.py
View File

@ -1,14 +1,7 @@
import os
supported_ckpt_extensions = set(['.ckpt', '.pth'])
supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth'])
try:
import safetensors.torch
supported_ckpt_extensions.add('.safetensors')
supported_pt_extensions.add('.safetensors')
except:
print("Could not import safetensors, safetensors support disabled.")
supported_ckpt_extensions = set(['.ckpt', '.pth', '.safetensors'])
supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors'])
folder_names_and_paths = {}
@ -38,6 +31,8 @@ output_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "ou
temp_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")
input_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
filename_list_cache = {}
if not os.path.exists(input_directory):
os.makedirs(input_directory)
@ -118,12 +113,18 @@ def get_folder_paths(folder_name):
return folder_names_and_paths[folder_name][0][:]
def recursive_search(directory):
if not os.path.isdir(directory):
return [], {}
result = []
dirs = {directory: os.path.getmtime(directory)}
for root, subdir, file in os.walk(directory, followlinks=True):
for filepath in file:
#we os.path,join directory with a blank string to generate a path separator at the end.
result.append(os.path.join(root, filepath).replace(os.path.join(directory,''),''))
return result
for d in subdir:
path = os.path.join(root, d)
dirs[path] = os.path.getmtime(path)
return result, dirs
def filter_files_extensions(files, extensions):
return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions, files)))
@ -132,20 +133,55 @@ def filter_files_extensions(files, extensions):
def get_full_path(folder_name, filename):
global folder_names_and_paths
if folder_name not in folder_names_and_paths:
return None
folders = folder_names_and_paths[folder_name]
filename = os.path.relpath(os.path.join("/", filename), "/")
for x in folders[0]:
full_path = os.path.join(x, filename)
if os.path.isfile(full_path):
return full_path
return None
def get_filename_list(folder_name):
def get_filename_list_(folder_name):
global folder_names_and_paths
output_list = set()
folders = folder_names_and_paths[folder_name]
output_folders = {}
for x in folders[0]:
output_list.update(filter_files_extensions(recursive_search(x), folders[1]))
return sorted(list(output_list))
files, folders_all = recursive_search(x)
output_list.update(filter_files_extensions(files, folders[1]))
output_folders = {**output_folders, **folders_all}
return (sorted(list(output_list)), output_folders)
def cached_filename_list_(folder_name):
global filename_list_cache
global folder_names_and_paths
if folder_name not in filename_list_cache:
return None
out = filename_list_cache[folder_name]
for x in out[1]:
time_modified = out[1][x]
folder = x
if os.path.getmtime(folder) != time_modified:
return None
folders = folder_names_and_paths[folder_name]
for x in folders[0]:
if x not in out[1]:
return None
return out
def get_filename_list(folder_name):
out = cached_filename_list_(folder_name)
if out is None:
out = get_filename_list_(folder_name)
global filename_list_cache
filename_list_cache[folder_name] = out
return list(out[0])
def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height=0):
def map_filename(filename):

36
nodes.py
View File

@ -17,7 +17,7 @@ import safetensors.torch
sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
import comfy.diffusers_convert
import comfy.diffusers_load
import comfy.samplers
import comfy.sample
import comfy.sd
@ -377,7 +377,7 @@ class DiffusersLoader:
model_path = path
break
return comfy.diffusers_convert.load_diffusers(model_path, fp16=comfy.model_management.should_use_fp16(), output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings"))
return comfy.diffusers_load.load_diffusers(model_path, fp16=comfy.model_management.should_use_fp16(), output_vae=output_vae, output_clip=output_clip, embedding_directory=folder_paths.get_folder_paths("embeddings"))
class unCLIPCheckpointLoader:
@ -426,6 +426,9 @@ class LoraLoader:
CATEGORY = "loaders"
def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
if strength_model == 0 and strength_clip == 0:
return (model, clip)
lora_path = folder_paths.get_full_path("loras", lora_name)
model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora_path, strength_model, strength_clip)
return (model_lora, clip_lora)
@ -507,6 +510,9 @@ class ControlNetApply:
CATEGORY = "conditioning"
def apply_controlnet(self, conditioning, control_net, image, strength):
if strength == 0:
return (conditioning, )
c = []
control_hint = image.movedim(-1,1)
for t in conditioning:
@ -613,6 +619,9 @@ class unCLIPConditioning:
CATEGORY = "conditioning"
def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation):
if strength == 0:
return (conditioning, )
c = []
for t in conditioning:
o = t[1].copy()
@ -749,7 +758,7 @@ class RepeatLatentBatch:
return (s,)
class LatentUpscale:
upscale_methods = ["nearest-exact", "bilinear", "area"]
upscale_methods = ["nearest-exact", "bilinear", "area", "bislerp"]
crop_methods = ["disabled", "center"]
@classmethod
@ -768,6 +777,25 @@ class LatentUpscale:
s["samples"] = comfy.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop)
return (s,)
class LatentUpscaleBy:
upscale_methods = ["nearest-exact", "bilinear", "area", "bislerp"]
@classmethod
def INPUT_TYPES(s):
return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,),
"scale_by": ("FLOAT", {"default": 1.5, "min": 0.01, "max": 8.0, "step": 0.01}),}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "upscale"
CATEGORY = "latent"
def upscale(self, samples, upscale_method, scale_by):
s = samples.copy()
width = round(samples["samples"].shape[3] * scale_by)
height = round(samples["samples"].shape[2] * scale_by)
s["samples"] = comfy.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled")
return (s,)
class LatentRotate:
@classmethod
def INPUT_TYPES(s):
@ -1244,6 +1272,7 @@ NODE_CLASS_MAPPINGS = {
"VAELoader": VAELoader,
"EmptyLatentImage": EmptyLatentImage,
"LatentUpscale": LatentUpscale,
"LatentUpscaleBy": LatentUpscaleBy,
"LatentFromBatch": LatentFromBatch,
"RepeatLatentBatch": RepeatLatentBatch,
"SaveImage": SaveImage,
@ -1322,6 +1351,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"LatentCrop": "Crop Latent",
"EmptyLatentImage": "Empty Latent Image",
"LatentUpscale": "Upscale Latent",
"LatentUpscaleBy": "Upscale Latent By",
"LatentComposite": "Latent Composite",
"LatentFromBatch" : "Latent From Batch",
"RepeatLatentBatch": "Repeat Latent Batch",

36
server.py
View File

@ -22,7 +22,7 @@ except ImportError:
import mimetypes
from comfy.cli_args import args
import comfy.utils
@web.middleware
async def cache_control(request: web.Request, handler):
@ -257,6 +257,29 @@ class PromptServer():
return web.Response(status=404)
@routes.get("/view_metadata/{folder_name}")
async def view_metadata(request):
folder_name = request.match_info.get("folder_name", None)
if folder_name is None:
return web.Response(status=404)
if not "filename" in request.rel_url.query:
return web.Response(status=404)
filename = request.rel_url.query["filename"]
if not filename.endswith(".safetensors"):
return web.Response(status=404)
safetensors_path = folder_paths.get_full_path(folder_name, filename)
if safetensors_path is None:
return web.Response(status=404)
out = comfy.utils.safetensors_header(safetensors_path, max_size=1024*1024)
if out is None:
return web.Response(status=404)
dt = json.loads(out)
if not "__metadata__" in dt:
return web.Response(status=404)
return web.json_response(dt["__metadata__"])
@routes.get("/prompt")
async def get_prompt(request):
return web.json_response(self.get_queue_info())
@ -272,6 +295,11 @@ class PromptServer():
info['display_name'] = nodes.NODE_DISPLAY_NAME_MAPPINGS[node_class] if node_class in nodes.NODE_DISPLAY_NAME_MAPPINGS.keys() else node_class
info['description'] = ''
info['category'] = 'sd'
if hasattr(obj_class, 'OUTPUT_NODE') and obj_class.OUTPUT_NODE == True:
info['output_node'] = True
else:
info['output_node'] = False
if hasattr(obj_class, 'CATEGORY'):
info['category'] = obj_class.CATEGORY
return info
@ -333,12 +361,12 @@ class PromptServer():
prompt_id = str(uuid.uuid4())
outputs_to_execute = valid[2]
self.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute))
return web.json_response({"prompt_id": prompt_id})
return web.json_response({"prompt_id": prompt_id, "number": number})
else:
print("invalid prompt:", valid[1])
return web.json_response({"error": valid[1]}, status=400)
return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400)
else:
return web.json_response({"error": "no prompt"}, status=400)
return web.json_response({"error": "no prompt", "node_errors": []}, status=400)
@routes.post("/queue")
async def post_queue(request):

10
web/extensions/core/colorPalette.js
View File

@ -174,7 +174,7 @@ const els = {}
// const ctxMenu = LiteGraph.ContextMenu;
app.registerExtension({
name: id,
init() {
addCustomNodeDefs(node_defs) {
const sortObjectKeys = (unordered) => {
return Object.keys(unordered).sort().reduce((obj, key) => {
obj[key] = unordered[key];
@ -182,10 +182,10 @@ app.registerExtension({
}, {});
};
const getSlotTypes = async () => {
function getSlotTypes() {
var types = [];
const defs = await api.getNodeDefs();
const defs = node_defs;
for (const nodeId in defs) {
const nodeData = defs[nodeId];
@ -212,8 +212,8 @@ app.registerExtension({
return types;
};
const completeColorPalette = async (colorPalette) => {
var types = await getSlotTypes();
function completeColorPalette(colorPalette) {
var types = getSlotTypes();
for (const type of types) {
if (!colorPalette.colors.node_slot[type]) {

2
web/index.html
View File

@ -14,5 +14,5 @@
window.graph = app.graph;
</script>
</head>
<body></body>
<body class="litegraph"></body>
</html>

6
web/scripts/api.js
View File

@ -88,6 +88,12 @@ class ComfyApi extends EventTarget {
case "executed":
this.dispatchEvent(new CustomEvent("executed", { detail: msg.data }));
break;
case "execution_start":
this.dispatchEvent(new CustomEvent("execution_start", { detail: msg.data }));
break;
case "execution_error":
this.dispatchEvent(new CustomEvent("execution_error", { detail: msg.data }));
break;
default:
if (this.#registered.has(msg.type)) {
this.dispatchEvent(new CustomEvent(msg.type, { detail: msg.data }));

111
web/scripts/app.js
View File

@ -771,16 +771,27 @@ export class ComfyApp {
LGraphCanvas.prototype.drawNodeShape = function (node, ctx, size, fgcolor, bgcolor, selected, mouse_over) {
const res = origDrawNodeShape.apply(this, arguments);
const nodeErrors = self.lastPromptError?.node_errors[node.id];
let color = null;
let lineWidth = 1;
if (node.id === +self.runningNodeId) {
color = "#0f0";
} else if (self.dragOverNode && node.id === self.dragOverNode.id) {
color = "dodgerblue";
}
else if (self.lastPromptError != null && nodeErrors?.errors) {
color = "red";
lineWidth = 2;
}
else if (self.lastExecutionError && +self.lastExecutionError.node_id === node.id) {
color = "#f0f";
lineWidth = 2;
}
if (color) {
const shape = node._shape || node.constructor.shape || LiteGraph.ROUND_SHAPE;
ctx.lineWidth = 1;
ctx.lineWidth = lineWidth;
ctx.globalAlpha = 0.8;
ctx.beginPath();
if (shape == LiteGraph.BOX_SHAPE)
@ -807,11 +818,28 @@ export class ComfyApp {
ctx.stroke();
ctx.strokeStyle = fgcolor;
ctx.globalAlpha = 1;
}
if (self.progress) {
ctx.fillStyle = "green";
ctx.fillRect(0, 0, size[0] * (self.progress.value / self.progress.max), 6);
ctx.fillStyle = bgcolor;
if (self.progress && node.id === +self.runningNodeId) {
ctx.fillStyle = "green";
ctx.fillRect(0, 0, size[0] * (self.progress.value / self.progress.max), 6);
ctx.fillStyle = bgcolor;
}
// Highlight inputs that failed validation
if (nodeErrors) {
ctx.lineWidth = 2;
ctx.strokeStyle = "red";
for (const error of nodeErrors.errors) {
if (error.extra_info && error.extra_info.input_name) {
const inputIndex = node.findInputSlot(error.extra_info.input_name)
if (inputIndex !== -1) {
let pos = node.getConnectionPos(true, inputIndex);
ctx.beginPath();
ctx.arc(pos[0] - node.pos[0], pos[1] - node.pos[1], 12, 0, 2 * Math.PI, false)
ctx.stroke();
}
}
}
}
@ -869,6 +897,17 @@ export class ComfyApp {
}
});
api.addEventListener("execution_start", ({ detail }) => {
this.lastExecutionError = null
});
api.addEventListener("execution_error", ({ detail }) => {
this.lastExecutionError = detail;
const formattedError = this.#formatExecutionError(detail);
this.ui.dialog.show(formattedError);
this.canvas.draw(true, true);
});
api.init();
}
@ -971,6 +1010,11 @@ export class ComfyApp {
const app = this;
// Load node definitions from the backend
const defs = await api.getNodeDefs();
await this.registerNodesFromDefs(defs);
await this.#invokeExtensionsAsync("registerCustomNodes");
}
async registerNodesFromDefs(defs) {
await this.#invokeExtensionsAsync("addCustomNodeDefs", defs);
// Generate list of known widgets
@ -1043,8 +1087,6 @@ export class ComfyApp {
LiteGraph.registerNodeType(nodeId, node);
node.category = nodeData.category;
}
await this.#invokeExtensionsAsync("registerCustomNodes");
}
/**
@ -1243,6 +1285,43 @@ export class ComfyApp {
return { workflow, output };
}
#formatPromptError(error) {
if (error == null) {
return "(unknown error)"
}
else if (typeof error === "string") {
return error;
}
else if (error.stack && error.message) {
return error.toString()
}
else if (error.response) {
let message = error.response.error.message;
if (error.response.error.details)
message += ": " + error.response.error.details;
for (const [nodeID, nodeError] of Object.entries(error.response.node_errors)) {
message += "\n" + nodeError.class_type + ":"
for (const errorReason of nodeError.errors) {
message += "\n - " + errorReason.message + ": " + errorReason.details
}
}
return message
}
return "(unknown error)"
}
#formatExecutionError(error) {
if (error == null) {
return "(unknown error)"
}
const traceback = error.traceback.join("")
const nodeId = error.node_id
const nodeType = error.node_type
return `Error occurred when executing ${nodeType}:\n\n${error.exception_message}\n\n${traceback}`
}
async queuePrompt(number, batchCount = 1) {
this.#queueItems.push({ number, batchCount });
@ -1250,8 +1329,10 @@ export class ComfyApp {
if (this.#processingQueue) {
return;
}
this.#processingQueue = true;
this.lastPromptError = null;
try {
while (this.#queueItems.length) {
({ number, batchCount } = this.#queueItems.pop());
@ -1278,7 +1359,12 @@ export class ComfyApp {
try {
await api.queuePrompt(number, p);
} catch (error) {
this.ui.dialog.show(error.response.error || error.toString());
const formattedError = this.#formatPromptError(error)
this.ui.dialog.show(formattedError);
if (error.response) {
this.lastPromptError = error.response;
this.canvas.draw(true, true);
}
break;
}
@ -1357,6 +1443,11 @@ export class ComfyApp {
const def = defs[node.type];
// HOTFIX: The current patch is designed to prevent the rest of the code from breaking due to primitive nodes,
// and additional work is needed to consider the primitive logic in the refresh logic.
if(!def)
continue;
for(const widgetNum in node.widgets) {
const widget = node.widgets[widgetNum]
if(widget.type == "combo" && def["input"]["required"][widget.name] !== undefined) {
@ -1376,6 +1467,8 @@ export class ComfyApp {
*/
clean() {
this.nodeOutputs = {};
this.lastPromptError = null;
this.lastExecutionError = null;
}
}

1
web/scripts/pnginfo.js
View File

@ -69,6 +69,7 @@ export async function importA1111(graph, parameters) {
const embeddings = await api.getEmbeddings();
const opts = parameters
.substr(p)
.split("\n")[1]
.split(",")
.reduce((p, n) => {
const s = n.split(":");

5
web/style.css
View File

@ -289,6 +289,11 @@ button.comfy-queue-btn {
/* Context menu */
.litegraph .dialog {
z-index: 1;
font-family: Arial;
}
.litegraph .litemenu-entry.has_submenu {
position: relative;
padding-right: 20px;